Huainan Normal University

Abstract In this study, ZnFe 2 O 4 was synthesized at 200°C via a solvothermal pathway using acetilacetonate salts of zinc and iron as raw materials in self-developed magnetic fields of varying intensities. The following phenomena were observed. During the synthesis process, applying a magnetic field causes spherical assemblies with micrometer-scale diameters to transform into coarse chain-like aggregates exhibiting a length-to-diameter ratio of approximately 4.8. As the strength of the magnetic field increases, the ZnFe 2 O 4 particle size gradually decreases. The synthetic magnetic field causes the magnetization value of ZnFe 2 O 4 to increase or decrease. This is due to the interaction between surface spins and bulk spins, and this interaction is regulated by the particle size. The zero-field-cooling (ZFC) curves measured under a 100-Oe magnetic field obey the Curie-Weiss law in the high-temperature region. The effective magnetic moments of the superparamagnetic particles obtained through fitting are μ sp = 2.53×10 4 μ B (ZnFe 2 O 4 prepared under the condition without magnetic field), μ sp = 1.69×10 4 μ B (ZnFe 2 O 4 prepared under the condition of two magnets), and μ sp = 1.85×10 4 μ B (ZnFe 2 O 4 prepared under the condition of four magnets). The estimated magnetic particle sizes are 11.4, 10.6, and 9.9 nm, respectively, which are larger than the corresponding sizes (8.6, 7.2, and 6.2 nm) obtained from electron microscopy, indicating that the magnetic moments of adjacent particles tend to be parallel. This work reports a new approach that enables the preparation of magnetic particles with clean surfaces as well as tunable sizes, morphologies, and properties simply by adjusting the magnetic field strength without the need for any additives or templates, thus broadening their potential for various applications.

Rural revitalization hinges on the rural agriculture–industry–services (AIS) integration and urban-rural coordinated development, with improved market-oriented allocation of rural factors serving as a key economic lever for advancing both. Based on national panel data from 2009 to 2022, our analysis suggests that rural AIS integration exerts a positive income-increasing effect on rural residents. Further quantile regression reveals that rural AIS integration exhibits a stronger positive association with farmers’ income growth at higher income quantiles. Both the dynamic System GMM and the difference GMM indicate that the rural AIS integration can narrow the urban-rural income gap. Non-farm employment plays a partial mediating role in the impact of rural AIS integration on reducing the income gap. The threshold model test results reveal that the income-increasing effect of the rural AIS integration showed an inverted U-shaped marginal convergence trend with the increase of urbanization rate. Therefore, deepening the reform of the rural land system, exploring multifunctional agriculture for higher non-farm employment, enriching the financial and fiscal support, and advancing phased coordination between urbanization and rural AIS integration are appropriate choices to promote the rural AIS integration, which aims to narrow the urban-rural income gap by creating new pathways to increase rural residents’ property income, operating income and wage income. The findings enrich the theoretical foundation for understanding how the rural AIS integration boosts residents’ income and narrows the urban-rural gap, with two specific contributions: first, clarifying that its marginal income-increasing effect is more pronounced across higher quantiles of rural income distribution; second, deriving policy implications for leveraging urbanization’s non-linear impact to strengthen this effect.

Ultraviolet absorption spectroscopy, recognized for its rapid, nondestructive, and noninvasive nature, has been widely employed in fields such as environmental monitoring and chemical analysis. However, the accuracy of quantitative spectral analysis remains challenged by data distortions arising from various factors in the instrumental transmission process, including particle scattering, spectral drift, excessive denoising, and fitting errors. To overcome these issues, we propose a specific regression approach that integrates variational mode decomposition (VMD) with a convolutional neural network (CNN) for absorption spectra, termed VMD-CNN. The method first decomposes the spectrum into a series of intrinsic mode functions, from which mode components associated with gas absorption characteristics are extracted. A one-dimensional CNN is then trained on a large dataset of spectra with known concentrations to establish a quantitative inversion model. Experiments conducted on NO and SO2 absorption spectra within the 200 to 226 nm range demonstrate the effectiveness of the proposed approach. Comparative analyses with conventional techniques—including nonlinear least squares, partial least squares, and fast Fourier transform amplitude methods—before and after noise reduction and background elimination show that traditional methods heavily depend on data quality and may produce distorted results under low-concentration conditions due to over-optimization. By contrast, the proposed VMD-CNN model, which requires no preprocessing, consistently achieves the lowest error metrics across broad concentration ranges, specifically yielding root mean square error as low as 12.0 for NO and 2.0 for SO2, with mean absolute percentage error maintained 1.8% and 2.35%, respectively.

Sensitive quantitative detection of breast cancer gene synthetic sequences is crucial for related biosensing research. To address the limitations of traditional sensors for detecting ultra-low concentrations, this study developed a novel fiber-optic biosensor by combining nanomaterial sensitization with nanoparticle signal amplification strategies. A fiber optic sensor based on single-mode fiber-thin-core fiber-multimode fiber-single-mode fiber structure was fabricated and functionalized with black phosphorus (BP) nano-interface. The Au@cDNA complex was prepared by covalently immobilizing sulfhydryl-modified complementary DNA (cDNA) on the surface of gold nanoparticles (AuNPs). The complex specifically hybridized with the probe DNA (pDNA) immobilized on the surface of the sensor. The experimental results show that this sensor has a sensitivity of 0.793 nm/lgM and a detection limit of 20.27 fM in the concentration range of 100 fM to 100 nM. Specifically, the BP-functionalized sensor exhibits superior dynamic range, higher sensitivity, and lower detection limits for detecting Au@cDNA. The synergistic effect of interfacial sensitization by BP and signal amplification by AuNPs significantly enhances detection performance, providing a promising platform for ultra-sensitive biosensing applications.

This article investigates the practically fixed-time synchronization problem for uncertain chaotic systems. First, a practically fixed-time stability criterion is introduced. Based on this criterion, an adaptive backstepping control technique is employed to design the controller. Unlike traditional dynamic surface control strategies, the proposed approach incorporates a continuous piecewise function. Theoretical analysis demonstrates that the proposed control strategy ensures the practically fixed-time stability of the closed-loop system, with the synchronization error converging to a small neighborhood of the origin within a fixed time. Finally, a simulation example is provided to verify the feasibility and effectiveness of the proposed method.

SummaryIsopropanol (IPA), a volatile organic compound linked to diabetic ketosis (DK), has emerged as a critical biomarker for early diabetes detection and management. Developing portable, room-temperature sensors for exhaled IPA analysis could enable non-invasive, point-of-care diagnostics. Here, we present a compact, surface plasmon resonance (SPR) fiber-optic sensor functionalized with Pt-doped ZnO nanoparticles for ultrasensitive IPA detection at room temperature. This material design amplifies the sensing performance through a synergetic effect of the catalysis of oxygen chemisorption on the ZnO and electronic sensitization by the Pt/ZnO Schottky junction. Systematic evaluation demonstrates that operating stably at 26 °C, the sensor exhibits a remarkable sensitivity of −0.405 a.u./ppm, a detection limit of 7.407 ppm, and high interference resistance. This successful integration of nanomaterial-enhanced sensitivity with a portable fiber-optic platform offers a transformative route for developing accessible tools for early diabetes detection and personalized metabolic monitoring.